Automatic Menu Recommendation for Industrial Catering Using Constraint-Based Filtering, K-Means Clustering, and Apriori Association Rule Mining
Perancangan Sistem Rekomendasi Menu Otomatis Berbasis Machine Learning Untuk Optimalisasi Perencanaan ‘Puteri Catering’
DOI:
https://doi.org/10.21070/ups.12024Keywords:
Apriori Association Rules, Constraint-Based Filtering, K-Means Clustering, Menu RecommendationsAbstract
Industrial catering providers such as Puteri Catering in Pandaan, Indonesia, face a persistent problem in planning daily menus that jointly satisfy cost limits, ingredient availability, and variety, particularly when the process is still done manually. This study addresses that problem by developing an automatic menu recommendation system that integrates Constraint-Based Filtering, K-Means Clustering, and Apriori Association Rule Mining, implemented with Laravel 11, MySQL, and Python (Scikit-learn, MLxtend) on a dataset of 43 menus, 62 ingredients, and 60 transactions. The system achieved a Silhouette Score of 0.5794, generated 83 association rules with the strongest reaching 88.9% confidence, and passed Black Box Testing across 47 test cases with a 100% success rate. These results confirm that the proposed hybrid approach offers a practical, data-driven decision support tool, enabling catering administrators to select cost-appropriate daily menu packages more efficiently and consistently.
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